CN116080326A - Semi-active suspension control method and system - Google Patents
Semi-active suspension control method and system Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
- B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
- B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
- B60G17/018—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by the use of a specific signal treatment or control method
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
- B60G17/06—Characteristics of dampers, e.g. mechanical dampers
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Abstract
The application discloses a control method and a system of a semi-active suspension, comprising the following steps: determining an external characteristic curve of the shock absorber according to a pre-established variable damping shock absorber simulation model; acquiring random disturbance information of a road surface and performance parameters of a semi-active suspension; determining a fuzzy regulation strategy according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface; according to the fuzzy regulation strategy, the regulated control parameters are determined, and the suspension dynamic deflection, the wheel dynamic load and the vehicle body acceleration are regulated according to the regulated control parameters, so that the peak values of the suspension dynamic deflection, the vehicle body acceleration and the vehicle body dynamic load can be effectively reduced.
Description
Technical Field
The application belongs to the technical field of control systems, and particularly relates to a control method and system of a semi-active suspension.
Background
The suspension is an important component of modern automobiles, and has important influence on riding comfort and running safety. The traditional passive suspension system has the advantages of no external energy input, simple structure and wide application, but only consumes energy when the suspension works, and the damping characteristic cannot be changed along with road surface excitation. The active suspension can obtain a high-quality vibration isolation system to realize the control target of an ideal suspension, but has the advantages of high energy consumption, high cost and complex structure. The semi-active suspension is passively controlled, the actuator is low in price, low in energy consumption and simple in structure, and the control quality of the semi-active suspension is close to that of the active suspension.
In recent years, control techniques based on fuzzy control and PID control are rapidly developed. Different control modes are combined, and the respective advantages are adopted, so that a better optimization effect can be obtained. If the incremental algorithm is combined with the PID controller, the performance of the semi-active suspension system is improved, and the running smoothness and the operation stability of the engineering vehicle are improved. And the parameters of a PID sliding mode controller (SMC-PID) are set by adopting a particle swarm optimization algorithm, so that the effect of the controller is improved, and the performance of the magnetorheological damper is improved.
Currently, when a suspension system algorithm is researched, a shock absorber is generally simplified into a spring model, and the external characteristics of the shock absorber are ignored, so that errors exist in calculation and actual processes of the semi-active suspension algorithm, and the popularization of the suspension system algorithm is limited.
Disclosure of Invention
The application aims to provide a control method and a system of a semi-active suspension, so as to solve the defects in the prior art, and the technical problem to be solved by the application is realized through the following technical scheme.
In a first aspect, an embodiment of the present application provides a method for controlling a semi-active suspension, where the method includes:
determining an external characteristic curve of the shock absorber according to a pre-established variable damping shock absorber simulation model;
acquiring random disturbance information of a road surface and performance parameters of a semi-active suspension;
determining a fuzzy regulation strategy according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface;
and determining the adjusted control parameters according to the fuzzy adjustment strategy, and adjusting the suspension dynamic deflection, the wheel dynamic load and the vehicle body acceleration according to the adjusted control parameters.
Optionally, the input of the pre-established variable damping vibration damper simulation model is different in input current and excitation speed, and the output is the external characteristic curve.
Optionally, the road surface random disturbance information is an identification of a road surface unevenness coefficient.
Optionally, the pre-established fuzzy adjustment strategy includes determining the scaling factor of the corresponding argument domain by the input variable bias and the input variable bias change rate.
Optionally, the input variable deviation and the input variable deviation change rate include five first fuzzy sets, where the first fuzzy sets respectively represent different fuzzy states, and the first fuzzy sets include PB being positive, PM being median, PS being positive small, ZE being zero, NS being negative small, NM being negative medium, NB being negative large; the expansion factors of the variable domain comprise five second fuzzy sets, the second fuzzy sets represent the opening and closing control trend of the electromagnetic valve, and the second fuzzy sets comprise ZE being closed, S being small, M being medium, B being large and K being open.
In a second aspect, embodiments of the present application provide a control system for a semi-active suspension, the system comprising:
the first determining module is used for determining an external characteristic curve of the shock absorber according to a pre-established variable damping shock absorber simulation model;
the acquisition module is used for acquiring random disturbance information of the road surface and performance parameters of the semi-active suspension;
the second determining module is used for determining a fuzzy regulation strategy according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface;
the adjusting module is used for determining the adjusted control parameters according to the fuzzy adjusting strategy and adjusting the suspension deflection, the wheel dynamic load and the vehicle body acceleration according to the adjusted control parameters.
Optionally, the input of the pre-established variable damping vibration damper simulation model is different in input current and excitation speed, and the output is the external characteristic curve.
Optionally, the road surface random disturbance information is an identification of a road surface unevenness coefficient.
Optionally, the pre-established fuzzy adjustment strategy includes determining the scaling factor of the corresponding argument domain by the input variable bias and the input variable bias change rate.
Optionally, the input variable deviation and the input variable deviation change rate include five first fuzzy sets, where the first fuzzy sets respectively represent different fuzzy states, and the first fuzzy sets include PB being positive, PM being median, PS being positive small, ZE being zero, NS being negative small, NM being negative medium, NB being negative large; the expansion factors of the variable domain comprise five second fuzzy sets, the second fuzzy sets represent the opening and closing control trend of the electromagnetic valve, and the second fuzzy sets comprise ZE being closed, S being small, M being medium, B being large and K being open.
Embodiments of the present application include the following advantages:
according to the control method and the control system of the semi-active suspension, the external characteristic curve of the shock absorber is determined according to the pre-established variable damping shock absorber simulation model; acquiring random disturbance information of a road surface and performance parameters of a semi-active suspension; determining a fuzzy regulation strategy according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface; according to a fuzzy regulation strategy, regulated control parameters are determined, and according to the regulated control parameters, the suspension dynamic deflection, the wheel dynamic load and the vehicle body acceleration are regulated, the external characteristics of the shock absorber are determined through a built variable damping shock absorber simulation model, the external characteristics of the shock absorber are combined with a semi-active suspension, an adaptive variable domain fuzzy PID control strategy is determined according to uncertainty of semi-active suspension parameters and random disturbance of a road surface, regulated control parameters are determined according to the adaptive variable domain fuzzy PID control strategy, and according to the regulated control parameters, the suspension dynamic deflection, the wheel dynamic load and the vehicle body acceleration are regulated, so that peak values of the suspension dynamic deflection, the vehicle body acceleration and the vehicle body dynamic load can be effectively reduced.
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In order to more clearly illustrate the embodiments or prior art solutions of the present application, the drawings that are required for the description of the embodiments or prior art will be briefly described below, it being apparent that the drawings in the following description are only some of the embodiments described in the present application, and that other drawings may be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flow chart of a method of controlling a semi-active suspension in accordance with one embodiment of the present application;
FIG. 2 is a schematic diagram of a control system for a semi-active suspension according to one embodiment of the present application;
FIG. 3 is a schematic diagram of a 1/4 vehicle semi-active suspension system in accordance with one embodiment of the present application;
FIG. 4 is a graph of B-stage road surface random excitation signals according to an embodiment of the present application;
FIG. 5 is a flow chart of the design of an adaptive fuzzy PID controller according to an embodiment of the present application;
FIG. 6 is a membership function of E, EC in one embodiment of the present application;
FIG. 7 is a schematic diagram of variable domain fuzzy control in an embodiment of the present application;
FIG. 8 is a block diagram of an embodiment of a control system for a semi-active suspension of the present application.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
An embodiment of the application provides a control method of a semi-active suspension, which is used for controlling the semi-active suspension. The execution body of the embodiment is a control system of a semi-active suspension.
Referring to fig. 1, a flowchart illustrating steps of an embodiment of a method for controlling a semi-active suspension of the present application may specifically include the steps of:
s101, determining an external characteristic curve of the shock absorber according to a pre-established variable damping shock absorber simulation model;
specifically, the input of the pre-established variable damping vibration damper simulation model is different in input current and excitation speed, and the output is an external characteristic curve.
In the bench test, the change of input current and excitation speed can obtain different external characteristic curves of the damper, the change of external characteristics of the damper can guide the damper, a corresponding variable damping damper simulation model is built according to the CDC damper data model obtained through the bench index test bench, and structural parameters consistent with the bench test are set to simulate to obtain the external characteristic curves of the damper.
S102, acquiring random disturbance information of a road surface and performance parameters of a semi-active suspension;
the road surface random disturbance information is the mark of the road surface unevenness coefficient.
Specifically, the performance parameters of the semi-active suspension include at least one or more of suspension dynamic deflection, wheel dynamic load, and vehicle body acceleration.
S103, determining a fuzzy regulation strategy according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface;
the pre-established fuzzy regulation strategy comprises the step of determining the telescopic factors of the corresponding argument domains through input variable deviation and input variable deviation change rate.
Specifically, the input variable deviation and the input variable deviation change rate include five first fuzzy sets, wherein the first fuzzy sets respectively represent different fuzzy states, and the first fuzzy sets include PB is positive, PM is median, PS is positive and small, ZE is zero, NS is negative and small, NM is negative and middle, and NB is negative and large; the expansion factors of the variable domain comprise five second fuzzy sets, wherein the second fuzzy sets represent the opening and closing control trend of the electromagnetic valve, and the second fuzzy sets comprise ZE being closed, S being small, M being medium, B being large and K being open.
Specifically, the control system of the semi-active suspension determines different fuzzy regulation strategies according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface, namely, different fuzzy regulation strategies, namely, the self-adaptive variable domain fuzzy PID control strategy, are obtained aiming at different performance parameters and the random disturbance information of the road surface.
S104, determining the adjusted control parameters according to the fuzzy adjustment strategy, and adjusting the suspension deflection, the wheel dynamic load and the vehicle body acceleration according to the adjusted control parameters.
Specifically, the fuzzy regulation strategy can recalculate the regulation parameters of PID to obtain the regulated control parameters, and regulate the performance parameters of the semi-active suspension through the regulated control parameters, namely, regulate the dynamic deflection of the suspension, the dynamic load of the wheels and the acceleration of the vehicle body.
According to the control method of the semi-active suspension, the external characteristic curve of the shock absorber is determined according to the pre-established variable damping shock absorber simulation model; acquiring random disturbance information of a road surface and performance parameters of a semi-active suspension; determining a fuzzy regulation strategy according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface; according to a fuzzy regulation strategy, regulated control parameters are determined, and according to the regulated control parameters, the suspension dynamic deflection, the wheel dynamic load and the vehicle body acceleration are regulated, the external characteristics of the shock absorber are determined through a built variable damping shock absorber simulation model, the external characteristics of the shock absorber are combined with a semi-active suspension, an adaptive variable domain fuzzy PID control strategy is determined according to uncertainty of semi-active suspension parameters and random disturbance of a road surface, regulated control parameters are determined according to the adaptive variable domain fuzzy PID control strategy, and according to the regulated control parameters, the suspension dynamic deflection, the wheel dynamic load and the vehicle body acceleration are regulated, so that peak values of the suspension dynamic deflection, the vehicle body acceleration and the vehicle body dynamic load can be effectively reduced.
A road surface adaptive semi-active suspension control system as shown in fig. 2, the semi-active suspension control system performing the steps of:
In the bench test, the change of input current and excitation speed can obtain different external characteristic curves of the shock absorber, and the change of the external characteristics of the shock absorber can guide the design of the shock absorber.
The hardware parameters hardware obtained by the test are shown in table 1:
TABLE 1
And establishing a corresponding variable damping vibration damper simulation model according to the CDC vibration damper data model obtained through the bench index test bench, setting structural parameters consistent with the bench test, and obtaining the external characteristic curve of the vibration damper through simulation.
The experimental pairs of the two are shown in table 2:
TABLE 2
As can be seen from table 2, the force values of the test results and the simulation results are basically consistent with the error range, the maximum error is 11.8%, the minimum error is 2.0%, and the above analysis can illustrate the accuracy of the shock absorber model, which shows that the model can be used for the research of semi-active suspension.
And 2, establishing a dynamic simplified model of a 1/4 vehicle semi-active suspension, selecting excitation patterns of different speeds on a B-level road surface as excitation signals of the semi-active suspension aiming at the semi-active suspension model, establishing a random road surface disturbance model, and inputting the random road surface disturbance model into the semi-active suspension model of the coupled CDC shock absorber as an initial excitation signal source.
The simplified model of the semi-active dynamics of the 1/4 vehicle is shown in fig. 3, and according to the semi-active suspension physical model and newton's second law, the kinematic differential equation of the sprung mass and the unsprung mass in the vertical Z direction is obtained as follows:
wherein M is b Is a sprung mass; m is M u Is an unsprung mass; k (K) s Is the suspension spring rate; k (K) w Is the tire stiffness; c (C) s Is the suspension damping coefficient; z is Z u Is unsprung mass displacement; z is Z b Is sprung mass displacement; z is Z w Inputting displacement for the road surface; u is the damping force of the semi-active suspension.
The state equation of the available system is:
wherein A satisfies:
b satisfies the following conditions:
f satisfies the following conditions:
F＝[00-10] T 。
wherein:
is an input of the road surface. />
Selecting the vibration acceleration of the car body
Suspension deflection (Z) b -Z u ) The method comprises the steps of carrying out a first treatment on the surface of the Tyre dynamic deformation (Z) u -Z w ) As output variables.
The output equation of the system is:
Y＝CX+DU
wherein Y satisfies:
wherein C satisfies:
d satisfies the following conditions:
according to different international standardization time domain disturbance curves under different road conditions, the road surface PSD is described. The following formula is typically used to fit the power spectrum of the road excitation:
wherein: spatial frequency of n (m -1 ) The method comprises the steps of carrying out a first treatment on the surface of the The reference spatial frequency is n 0 The method comprises the steps of carrying out a first treatment on the surface of the Normally take n0＝0.1(m -1 ). Road surface unevenness coefficient G q (n 0 )(m 3 ) The frequency index is 2, which is gaussian white noise.
The standard GB-7031-1986 divides the road surface unevenness coefficient into 8 grades, wherein the grade A road surface is a corresponding expressway and the road surface condition thereof, the grade A road surface is the best road surface condition, the grade E road surface is an unpaved road surface, and the grade H road surface is the worst road surface condition.
The road surface unevenness eight-stage classification criteria are shown in table 3:
TABLE 3 Table 3
And (3) establishing a filter white noise excitation signal of a random road surface disturbance model aiming at the semi-active suspension model, and selecting an excitation pattern when the vehicle speed on the B-level road surface is 30km/h, 70km/h and 120km/h as an excitation signal of the semi-active suspension.
The time domain signal of the road surface excitation is as follows:
wherein f 0 Q (t) is the road surface height (m), G q (n 0 ) Is the road surface unevenness coefficient (m 3 ) V is the vehicle running speed (m/s), and w (t) is the white noise signal.
The B-stage road surface random excitation signal is shown in fig. 4.
And step 3, establishing a self-adaptive variable domain fuzzy PID controller, wherein the PID controller is directly connected with the suspension system, and the fuzzy controller is connected with the PID controller. The PID controller adjusts PID parameters Kp, ki and Kd in real time, so that the semi-active suspension can respectively adjust suspension dynamic deflection, wheel dynamic load and vehicle body acceleration. The fuzzy controller can adjust the parameters of the PID controller in real time according to the dynamic performance of the automobile, and a two-input three-output two-dimensional fuzzy controller is provided, wherein the input variables of the fuzzy controller are the deviation E and the deviation change rate EC, and the output variables are the correction amounts Kp, ki and Kd of the parameters of the PID controller, as shown in figure 5.
According to an actual control object, the vibration speed deviation signal and the vibration acceleration deviation change rate in the vertical direction of the vehicle body are both in the form of numerical values, so that the input and output variables are subjected to fuzzification processing.
Five fuzzy sets are used to represent their fuzzy states for the input variables E, EC of the fuzzy controller, in the design, the fuzzy arguments of the input variables E and EC and the output variables Kp, ki and Kd are each set to [ -6, -5, -4, -3, -2, -1,0,1,2,3,4,5,6], and the fuzzy subset for the PID control E, EC is [ NB, NM, NS, ZO, PS, PM, PB ].
According to the previous analysis of the suspension, the membership functions are all triangular membership functions, the mamdani type min-max method is selected as a fuzzy reasoning method, the gravity center method is selected as a fuzzy decision, and E can be obtained, and the membership functions of EC are shown in figure 6.
The membership function expression mode is as follows:
taking the deviation E and the deviation EC as the input of the controller, and obtaining corrected PID parameters as follows:
K zp ＝K p0 +q kp K p
K zi ＝K i0 +q ki K i
K zd ＝K d0 +q kd K d
wherein: wherein: k (K) zp 、K zi 、K zd Setting the final PID parameters; k (K) p0 、K i0 、K d0 Setting values for PID initial parameters; q kp 、q ki And q kd Is a correction coefficient.
In order to avoid the phenomenon that the input and output variables are too large or too small relative to the domain, a domain-changing fuzzy control method is provided, an adaptive domain-changing fuzzy PID controller is established, and an adaptive domain-changing fuzzy control schematic diagram is shown in figure 7.
xi= [ -E, E ] (i=1, 2 …, n) is the argument of input variable xi (i=1, 2, …, n), y= [ -U, U ] is the argument of output variable Y.
The fuzzy output response may be approximated as:
the method can be changed into the following steps:
X i (x i )＝[-α i (x i )E i ,α i (x i )E i ]
Y(y)＝[-β(y)U,β(y)U]
wherein: variable alpha i (x i ) (i=1, 2, n), beta (y) is X i And the expansion factor of Y, alpha i (x i )＝1-λexp(-kx 2 ),λ∈(0,1),k>0。
The input variables of the selected controllers are deviation E and EC, and the output of the controllers are expansion factors of the variable domain respectively. Through the controller, three indexes of suspension dynamic deflection and vehicle body acceleration representing suspension comfort and vehicle body dynamic load representing safety are optimized. The corresponding expansion or expansion of the input and output theoretical control quantity domain along with the change of errors is ensured, so that the fuzzy control has the capability of adapting to the change of a control object, and the ideal control output is continuously approximated in a more accurate local area.
According to an actual control object, five fuzzy sets are adopted for the input deviation E and EC of the controller to represent fuzzy states of the input deviation E and EC, and corresponding fuzzy subsets are PB which are positive and big, PM which are median, PS which are positive and small, ZE which are zero, NS which are negative and small, NM which are negative and middle and NB which are negative and big; five fuzzy sets are adopted for the output expansion factors to represent the opening and closing control trend of the electromagnetic valve, namely ZE is closed, S is small, M is medium, B is large and K is open; . In the design, the fuzzy arguments of the input variable input and output are set to [ -6, -5, -4, -3, -2, -1,0,1,2,3,4,5,6].
From previous analysis of suspension motion process, a fuzzy rule table may be obtained as shown in table 4 below:
TABLE 4 Table 4
The fuzzy rule table may be specifically described as: when the input variable E is NB, the other input variable EC is NB, NM, NS, ZE, PS, PM, PB, and the corresponding output variables are PB, PB, PM, PS, PM, PB, PB respectively; when the input variable E is NM, the other input variable EC is NB, NM, NS, ZE, PS, PM, PB, and the corresponding output variables are PB, PM, PM, PS, PM, PM, PB respectively; when the input variable E is NS, the output variable corresponding to the other input variable EC NB, NM, NS, ZE, PS, PM, PB is PM, PM, PS, ZO, PS, PM, PM respectively; when the input variable E is ZE, the other input variable EC is NB, NM, NS, ZE, PS, PM, PB, and the corresponding output variables are PS, PS, ZO, ZO, ZO, PS, PS respectively; when the input variable E is PS, the other input variable EC is NB, NM, NS, ZE, PS, PM, PB, and the corresponding output variables are PM, PM, PS, ZO, PS, PM, PM respectively; when the input variable E is PM, the other input variable EC is NB, NM, NS, ZE, PS, PM, PB, and the corresponding output variables are PB, PM, PM, PS, PM, PM, PB respectively; when the input variable E is PB, the other input variable EC is NB, NM, NS, ZE, PS, PM, PB, and the corresponding output variables are PB, PB, PM, PS, PM, PB, PB, respectively.
The control system monitors corresponding output variables of the system, namely performance parameters of the semi-active suspension respectively, and comprises the following components: and evaluating indexes such as suspension deflection, vehicle body acceleration, vehicle body dynamic load and the like.
Based on B-class road surface excitation, 3 different vehicle running conditions are selected, namely low-speed running (30 km/h), medium-speed running (70 km/h) and high-speed running (120 km/h). And respectively carrying out simulation comparison on 3 evaluation indexes of vehicle body acceleration, suspension disturbance and wheel dynamic load selected under PID Control (PC), fuzzy Control (FC) and variable domain fuzzy PID self-adaptive control (VAC). On this basis, the simulation results were further analyzed and processed to obtain RMS values, which are shown in table 5.
TABLE 5
As can be seen from table 5, all three control strategies can reduce the vehicle body acceleration, suspension deflection and amplitude of wheel dynamic load under B-stage road excitation by the VAC controller, and all cases show that compared with other methods, the VAC controller can obtain good optimization effect.
It can be seen that the vehicle body acceleration, suspension dynamic deflection and wheel dynamic load of the self-adaptive variable domain fuzzy PID semi-active suspension control system are greatly improved compared with the traditional single fuzzy PID control system and fuzzy control, and the comfort, smoothness and steering stability are greatly improved.
In summary, according to the adaptive variable domain fuzzy PID semi-active suspension control system provided by the embodiment of the invention, the dynamic performance of the semi-active suspension system coupled with the CDC shock absorber is analyzed to obtain a control variable, and the mathematical function of the semi-active suspension is deduced. In addition, a semi-active suspension simulation model based on a shock absorber bench test is established, so that damping control of the semi-active suspension of the vehicle is more efficient and energy-saving. Firstly, measuring external characteristics by using a bench test bed and establishing a CDC shock absorber simulation model. And then, selecting excitation patterns of different speeds of a certain road surface as excitation signals of a semi-active suspension dynamics model of the vehicle to obtain a random road surface disturbance model. An adaptive variable domain fuzzy PID controller is established, the PID controller is directly connected with the suspension system, and the fuzzy controller is connected with the PID controller. The fuzzy controller takes the deviation E and the deviation EC as the input of the controller, and adopts a variable-domain fuzzy controller to carry out fuzzification processing on input and output variables. The established road model system is used as an initial excitation signal source to be input into a semi-active suspension model coupled with the CDC shock absorber, and then a variable-domain self-adaptive fuzzy PID control model is input into a control module of the suspension system, and corresponding parameters are fixed, so that different performance requirements of driving comfort and safety under different road surface disturbances are responded quickly. The invention can effectively reduce the peak value of the dynamic deflection of the rear suspension, the acceleration of the vehicle body and the dynamic load of the vehicle body, and improves the running smoothness of the vehicle.
According to the embodiment of the invention, the CDC shock absorber is tested on the bench index test bed, a corresponding variable damping shock absorber simulation model is established, structural parameters consistent with the bench test are set, and the external characteristic curve of the shock absorber is obtained through simulation. And (3) establishing a dynamic model of the 1/4 vehicle semi-active suspension, selecting excitation patterns of different speeds of a certain road surface as excitation signals of the semi-active suspension, and establishing a random road surface disturbance model. A two-input three-output two-dimensional fuzzy controller is provided, the deviation E and the EC are used as the input of the controller, and the input and output variables are subjected to fuzzification.
The established road model system is used as an initial excitation signal source to be input into a semi-active suspension model, a variable domain self-adaptive fuzzy PID control model is input into a control module of the suspension system, corresponding parameters are fixed, and a variable domain fuzzy controller is adopted. The input and output fields of the control system are self-set, so that the control precision is improved; and the dynamic and static characteristics of the system are improved by adopting self-adaptive fuzzy PID control.
Compared with the prior art, the embodiment of the application can realize:
the external characteristics of the shock absorber are obtained through bench experiments and simulations, the external characteristics of the shock absorber are combined with the semi-active suspension, and an adaptive variable domain fuzzy PID control strategy is provided for uncertainty of suspension parameters and random disturbance of a road surface, so that the peak value of dynamic deflection of the rear suspension, acceleration of the vehicle body and dynamic load of the vehicle body can be effectively reduced.
By effectively combining road surface information and semi-active suspension states, the adaptive variable-domain fuzzy PID controller is designed to quickly respond to different performance requirements of driving comfort and safety under different road surface disturbances, and vibration reduction and stability of the system are improved.
The direct relation between the suspension system and the shock absorber is established, in the bench test, the change of the input current and the excitation speed can obtain different external characteristic curves of the shock absorber, the design of the shock absorber can be guided, and compared with the selection of a single shock absorber, the shock absorber has a more comprehensive control effect.
Another embodiment of the present application provides a control system for a semi-active suspension, configured to execute the control method for a semi-active suspension provided in the foregoing embodiment.
Referring to fig. 8, there is shown a block diagram of an embodiment of a control system for a semi-active suspension of the present application, which may include the following modules: a first determining module 801, an acquiring module 802, a second determining module 803, and an adjusting module 804, wherein:
the first determining module 801 is configured to determine an external characteristic curve of the shock absorber according to a pre-established variable damping shock absorber simulation model;
the acquisition module 802 is used for acquiring random disturbance information of the road surface and performance parameters of the semi-active suspension;
the second determining module 803 is configured to determine a fuzzy adjustment policy according to the performance parameter of the semi-active suspension and the random disturbance information of the road surface;
the adjusting module 804 is configured to determine an adjusted control parameter according to a fuzzy adjustment policy, and adjust suspension dynamic deflection, wheel dynamic load, and vehicle body acceleration according to the adjusted control parameter.
According to the control system of the semi-active suspension, the external characteristic curve of the shock absorber is determined according to the pre-established variable damping shock absorber simulation model; acquiring random disturbance information of a road surface and performance parameters of a semi-active suspension; determining a fuzzy regulation strategy according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface; according to a fuzzy regulation strategy, regulated control parameters are determined, and according to the regulated control parameters, the suspension dynamic deflection, the wheel dynamic load and the vehicle body acceleration are regulated, the external characteristics of the shock absorber are determined through a built variable damping shock absorber simulation model, the external characteristics of the shock absorber are combined with a semi-active suspension, an adaptive variable domain fuzzy PID control strategy is determined according to uncertainty of semi-active suspension parameters and random disturbance of a road surface, regulated control parameters are determined according to the adaptive variable domain fuzzy PID control strategy, and according to the regulated control parameters, the suspension dynamic deflection, the wheel dynamic load and the vehicle body acceleration are regulated, so that peak values of the suspension dynamic deflection, the vehicle body acceleration and the vehicle body dynamic load can be effectively reduced.
In a further embodiment of the present application, the control system of the semi-active suspension provided in the foregoing embodiment is further described in additional detail.
Optionally, the input of the pre-established variable damping vibration damper simulation model is different in input current and excitation speed, and the output is an external characteristic curve.
Optionally, the road surface random disturbance information is an identification of a road surface unevenness coefficient.
Optionally, the pre-established fuzzy tuning strategy includes determining the scaling factor of the corresponding domain of the argument by the input variable bias and the input variable bias rate of change.
Optionally, the input variable deviation and the input variable deviation change rate include five first fuzzy sets, where the first fuzzy sets respectively represent different fuzzy states, and the first fuzzy sets include PB being positive, PM being median, PS being positive small, ZE being zero, NS being negative small, NM being negative median, NB being negative large; the expansion factors of the variable domain comprise five second fuzzy sets, wherein the second fuzzy sets represent the opening and closing control trend of the electromagnetic valve, and the second fuzzy sets comprise ZE being closed, S being small, M being medium, B being large and K being open.
For the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points.
According to the control system of the semi-active suspension, the external characteristic curve of the shock absorber is determined according to the pre-established variable damping shock absorber simulation model; acquiring random disturbance information of a road surface and performance parameters of a semi-active suspension; determining a fuzzy regulation strategy according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface; according to a fuzzy regulation strategy, regulated control parameters are determined, and according to the regulated control parameters, the suspension dynamic deflection, the wheel dynamic load and the vehicle body acceleration are regulated, the external characteristics of the shock absorber are determined through a built variable damping shock absorber simulation model, the external characteristics of the shock absorber are combined with a semi-active suspension, an adaptive variable domain fuzzy PID control strategy is determined according to uncertainty of semi-active suspension parameters and random disturbance of a road surface, regulated control parameters are determined according to the adaptive variable domain fuzzy PID control strategy, and according to the regulated control parameters, the suspension dynamic deflection, the wheel dynamic load and the vehicle body acceleration are regulated, so that peak values of the suspension dynamic deflection, the vehicle body acceleration and the vehicle body dynamic load can be effectively reduced.
It should be noted that the foregoing detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular is intended to include the plural unless the context clearly indicates otherwise. Furthermore, it will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, steps, operations, devices, components, and/or groups thereof.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or otherwise described herein.
Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Spatially relative terms, such as "above … …," "above … …," "upper surface at … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial location relative to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "above" or "over" other devices or structures would then be oriented "below" or "beneath" the other devices or structures. Thus, the exemplary term "above … …" may include both orientations of "above … …" and "below … …". The device may also be positioned in other different ways, such as rotated 90 degrees or at other orientations, and the spatially relative descriptors used herein interpreted accordingly.
In the above detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, like numerals typically identify like components unless context indicates otherwise. The illustrated embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (10)
1. A method of controlling a semi-active suspension, the method comprising:
determining an external characteristic curve of the shock absorber according to a pre-established variable damping shock absorber simulation model;
acquiring random disturbance information of a road surface and performance parameters of a semi-active suspension;
determining a fuzzy regulation strategy according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface;
and determining the adjusted control parameters according to the fuzzy adjustment strategy, and adjusting the suspension dynamic deflection, the wheel dynamic load and the vehicle body acceleration according to the adjusted control parameters.
2. The method according to claim 1, wherein the pre-established variable damping vibration damper simulation model is input with different input currents and excitation speeds, and is output with the outer characteristic curve.
3. The method for controlling a semi-active suspension according to claim 1, wherein the road surface random disturbance information is an identification of a road surface unevenness coefficient.
4. The method of claim 1, wherein the pre-established fuzzy tuning strategy includes determining the scaling factor of the corresponding variable domain by the input variable bias and the input variable bias rate of change.
5. The method of claim 4, wherein the input variable bias and the input variable bias change rate include five first fuzzy sets, the first fuzzy sets respectively representing different fuzzy states, the first fuzzy sets including PB positive, PM median, PS positive small, ZE zero, NS negative small, NM negative medium, NB negative large; the expansion factors of the variable domain comprise five second fuzzy sets, the second fuzzy sets represent the opening and closing control trend of the electromagnetic valve, and the second fuzzy sets comprise ZE being closed, S being small, M being medium, B being large and K being open.
6. A control system for a semi-active suspension, the system comprising:
the first determining module is used for determining an external characteristic curve of the shock absorber according to a pre-established variable damping shock absorber simulation model;
the acquisition module is used for acquiring random disturbance information of the road surface and performance parameters of the semi-active suspension;
the second determining module is used for determining a fuzzy regulation strategy according to the performance parameters of the semi-active suspension and the random disturbance information of the road surface;
the adjusting module is used for determining the adjusted control parameters according to the fuzzy adjusting strategy and adjusting the suspension deflection, the wheel dynamic load and the vehicle body acceleration according to the adjusted control parameters.
7. The control system of a semi-active suspension as recited in claim 6 wherein said pre-established variable damping shock absorber simulation model is input for different input currents and excitation speeds and output as said outer characteristic.
8. The control system of a semi-active suspension of claim 6, wherein the road surface random disturbance information is an identification of a road surface unevenness coefficient.
9. The control system of a semi-active suspension of claim 6, wherein the pre-established fuzzy tuning strategy includes determining a telescoping factor for a corresponding variable domain by an input variable bias and an input variable bias rate of change.
10. The control system of a semi-active suspension of claim 9, wherein the input variable bias and the input variable bias rate of change include five first fuzzy sets, the first fuzzy sets representing different fuzzy states, respectively, the first fuzzy sets including PB positive large, PM median, PS positive small, ZE zero, NS negative small, NM negative medium, NB negative large; the expansion factors of the variable domain comprise five second fuzzy sets, the second fuzzy sets represent the opening and closing control trend of the electromagnetic valve, and the second fuzzy sets comprise ZE being closed, S being small, M being medium, B being large and K being open.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116901639A (en) * | 2023-07-03 | 2023-10-20 | 盐城工学院 | Vibration reduction control method for active suspension of automobile |
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